feat(api): update via SDK Studio (#40)

.stats.yml

@@ -1,2 +1,2 @@
-configured_endpoints: 6
+configured_endpoints: 7
 openapi_spec_url: https://storage.googleapis.com/stainless-sdk-openapi-specs/groqcloud%2Fgroqcloud-c28de228634e737a173375583a09eef5e0d7fa81fcdf7090d14d194e6ef4fdc5.yml

api.md

@@ -1,3 +1,15 @@
+# Embeddings
+
+Types:
+
+```python
+from groq.types import EmbeddingCreateResponse
+```
+
+Methods:
+
+- <code title="post /openai/v1/embeddings">client.embeddings.<a href="./src/groq/resources/embeddings.py">create</a>(\*\*<a href="src/groq/types/embedding_create_params.py">params</a>) -> <a href="./src/groq/types/embedding_create_response.py">EmbeddingCreateResponse</a></code>
+
 # Chat
 
 ## Completions

src/groq/_client.py

@@ -46,6 +46,7 @@
 
 
 class Groq(SyncAPIClient):
+ embeddings: resources.EmbeddingsResource
  chat: resources.ChatResource
  audio: resources.AudioResource
  models: resources.ModelsResource
@@ -106,6 +107,7 @@ def __init__(
  _strict_response_validation=_strict_response_validation,
  )
 
+ self.embeddings = resources.EmbeddingsResource(self)
  self.chat = resources.ChatResource(self)
  self.audio = resources.AudioResource(self)
  self.models = resources.ModelsResource(self)
@@ -218,6 +220,7 @@ def _make_status_error(
 
 
 class AsyncGroq(AsyncAPIClient):
+ embeddings: resources.AsyncEmbeddingsResource
  chat: resources.AsyncChatResource
  audio: resources.AsyncAudioResource
  models: resources.AsyncModelsResource
@@ -278,6 +281,7 @@ def __init__(
  _strict_response_validation=_strict_response_validation,
  )
 
+ self.embeddings = resources.AsyncEmbeddingsResource(self)
  self.chat = resources.AsyncChatResource(self)
  self.audio = resources.AsyncAudioResource(self)
  self.models = resources.AsyncModelsResource(self)
@@ -391,27 +395,31 @@ def _make_status_error(
 
 class GroqWithRawResponse:
  def __init__(self, client: Groq) -> None:
+ self.embeddings = resources.EmbeddingsResourceWithRawResponse(client.embeddings)
  self.chat = resources.ChatResourceWithRawResponse(client.chat)
  self.audio = resources.AudioResourceWithRawResponse(client.audio)
  self.models = resources.ModelsResourceWithRawResponse(client.models)
 
 
 class AsyncGroqWithRawResponse:
  def __init__(self, client: AsyncGroq) -> None:
+ self.embeddings = resources.AsyncEmbeddingsResourceWithRawResponse(client.embeddings)
  self.chat = resources.AsyncChatResourceWithRawResponse(client.chat)
  self.audio = resources.AsyncAudioResourceWithRawResponse(client.audio)
  self.models = resources.AsyncModelsResourceWithRawResponse(client.models)
 
 
 class GroqWithStreamedResponse:
  def __init__(self, client: Groq) -> None:
+ self.embeddings = resources.EmbeddingsResourceWithStreamingResponse(client.embeddings)
  self.chat = resources.ChatResourceWithStreamingResponse(client.chat)
  self.audio = resources.AudioResourceWithStreamingResponse(client.audio)
  self.models = resources.ModelsResourceWithStreamingResponse(client.models)
 
 
 class AsyncGroqWithStreamedResponse:
  def __init__(self, client: AsyncGroq) -> None:
+ self.embeddings = resources.AsyncEmbeddingsResourceWithStreamingResponse(client.embeddings)
  self.chat = resources.AsyncChatResourceWithStreamingResponse(client.chat)
  self.audio = resources.AsyncAudioResourceWithStreamingResponse(client.audio)
  self.models = resources.AsyncModelsResourceWithStreamingResponse(client.models)

src/groq/resources/__init__.py

@@ -24,8 +24,22 @@
  ModelsResourceWithStreamingResponse,
  AsyncModelsResourceWithStreamingResponse,
 )
+from .embeddings import (
+ EmbeddingsResource,
+ AsyncEmbeddingsResource,
+ EmbeddingsResourceWithRawResponse,
+ AsyncEmbeddingsResourceWithRawResponse,
+ EmbeddingsResourceWithStreamingResponse,
+ AsyncEmbeddingsResourceWithStreamingResponse,
+)
 
 __all__ = [
+ "EmbeddingsResource",
+ "AsyncEmbeddingsResource",
+ "EmbeddingsResourceWithRawResponse",
+ "AsyncEmbeddingsResourceWithRawResponse",
+ "EmbeddingsResourceWithStreamingResponse",
+ "AsyncEmbeddingsResourceWithStreamingResponse",
  "ChatResource",
  "AsyncChatResource",
  "ChatResourceWithRawResponse",

src/groq/resources/embeddings.py

@@ -0,0 +1,203 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import List, Union, Optional
+from typing_extensions import Literal
+
+import httpx
+
+from ..types import embedding_create_params
+from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
+from .._utils import (
+ maybe_transform,
+ async_maybe_transform,
+)
+from .._compat import cached_property
+from .._resource import SyncAPIResource, AsyncAPIResource
+from .._response import (
+ to_raw_response_wrapper,
+ to_streamed_response_wrapper,
+ async_to_raw_response_wrapper,
+ async_to_streamed_response_wrapper,
+)
+from .._base_client import (
+ make_request_options,
+)
+from ..types.embedding_create_response import EmbeddingCreateResponse
+
+__all__ = ["EmbeddingsResource", "AsyncEmbeddingsResource"]
+
+
+class EmbeddingsResource(SyncAPIResource):
+ @cached_property
+ def with_raw_response(self) -> EmbeddingsResourceWithRawResponse:
+ return EmbeddingsResourceWithRawResponse(self)
+
+ @cached_property
+ def with_streaming_response(self) -> EmbeddingsResourceWithStreamingResponse:
+ return EmbeddingsResourceWithStreamingResponse(self)
+
+ def create(
+ self,
+ *,
+ input: Union[str, List[str]],
+ model: str,
+ dimensions: int | NotGiven = NOT_GIVEN,
+ encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+ user: Optional[str] | NotGiven = NOT_GIVEN,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Headers | None = None,
+ extra_query: Query | None = None,
+ extra_body: Body | None = None,
+ timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+ ) -> EmbeddingCreateResponse:
+ """
+ Creates an embedding vector representing the input text.
+
+ Args:
+ input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+ inputs in a single request, pass an array of strings or array of token arrays.
+ The input must not exceed the max input tokens for the model, cannot be an empty
+ string, and any array must be 2048 dimensions or less.
+
+ model: ID of the model to use.
+
+ dimensions: The number of dimensions to return the embeddings in.
+
+ encoding_format: The format to return the embeddings in.
+
+ user: A unique identifier representing your end-user, which can help us monitor and
+ detect abuse.
+
+ extra_headers: Send extra headers
+
+ extra_query: Add additional query parameters to the request
+
+ extra_body: Add additional JSON properties to the request
+
+ timeout: Override the client-level default timeout for this request, in seconds
+ """
+ return self._post(
+ "/openai/v1/embeddings",
+ body=maybe_transform(
+ {
+ "input": input,
+ "model": model,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ "user": user,
+ },
+ embedding_create_params.EmbeddingCreateParams,
+ ),
+ options=make_request_options(
+ extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
+ ),
+ cast_to=EmbeddingCreateResponse,
+ )
+
+
+class AsyncEmbeddingsResource(AsyncAPIResource):
+ @cached_property
+ def with_raw_response(self) -> AsyncEmbeddingsResourceWithRawResponse:
+ return AsyncEmbeddingsResourceWithRawResponse(self)
+
+ @cached_property
+ def with_streaming_response(self) -> AsyncEmbeddingsResourceWithStreamingResponse:
+ return AsyncEmbeddingsResourceWithStreamingResponse(self)
+
+ async def create(
+ self,
+ *,
+ input: Union[str, List[str]],
+ model: str,
+ dimensions: int | NotGiven = NOT_GIVEN,
+ encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+ user: Optional[str] | NotGiven = NOT_GIVEN,
+ # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+ # The extra values given here take precedence over values defined on the client or passed to this method.
+ extra_headers: Headers | None = None,
+ extra_query: Query | None = None,
+ extra_body: Body | None = None,
+ timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+ ) -> EmbeddingCreateResponse:
+ """
+ Creates an embedding vector representing the input text.
+
+ Args:
+ input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+ inputs in a single request, pass an array of strings or array of token arrays.
+ The input must not exceed the max input tokens for the model, cannot be an empty
+ string, and any array must be 2048 dimensions or less.
+
+ model: ID of the model to use.
+
+ dimensions: The number of dimensions to return the embeddings in.
+
+ encoding_format: The format to return the embeddings in.
+
+ user: A unique identifier representing your end-user, which can help us monitor and
+ detect abuse.
+
+ extra_headers: Send extra headers
+
+ extra_query: Add additional query parameters to the request
+
+ extra_body: Add additional JSON properties to the request
+
+ timeout: Override the client-level default timeout for this request, in seconds
+ """
+ return await self._post(
+ "/openai/v1/embeddings",
+ body=await async_maybe_transform(
+ {
+ "input": input,
+ "model": model,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ "user": user,
+ },
+ embedding_create_params.EmbeddingCreateParams,
+ ),
+ options=make_request_options(
+ extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
+ ),
+ cast_to=EmbeddingCreateResponse,
+ )
+
+
+class EmbeddingsResourceWithRawResponse:
+ def __init__(self, embeddings: EmbeddingsResource) -> None:
+ self._embeddings = embeddings
+
+ self.create = to_raw_response_wrapper(
+ embeddings.create,
+ )
+
+
+class AsyncEmbeddingsResourceWithRawResponse:
+ def __init__(self, embeddings: AsyncEmbeddingsResource) -> None:
+ self._embeddings = embeddings
+
+ self.create = async_to_raw_response_wrapper(
+ embeddings.create,
+ )
+
+
+class EmbeddingsResourceWithStreamingResponse:
+ def __init__(self, embeddings: EmbeddingsResource) -> None:
+ self._embeddings = embeddings
+
+ self.create = to_streamed_response_wrapper(
+ embeddings.create,
+ )
+
+
+class AsyncEmbeddingsResourceWithStreamingResponse:
+ def __init__(self, embeddings: AsyncEmbeddingsResource) -> None:
+ self._embeddings = embeddings
+
+ self.create = async_to_streamed_response_wrapper(
+ embeddings.create,
+ )

src/groq/types/__init__.py

@@ -5,3 +5,5 @@
 from .model import Model as Model
 from .model_list import ModelList as ModelList
 from .translation import Translation as Translation
+from .embedding_create_params import EmbeddingCreateParams as EmbeddingCreateParams
+from .embedding_create_response import EmbeddingCreateResponse as EmbeddingCreateResponse

src/groq/types/embedding_create_params.py

@@ -0,0 +1,33 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import List, Union, Optional
+from typing_extensions import Literal, Required, TypedDict
+
+__all__ = ["EmbeddingCreateParams"]
+
+
+class EmbeddingCreateParams(TypedDict, total=False):
+ input: Required[Union[str, List[str]]]
+ """Input text to embed, encoded as a string or array of tokens.
+
+ To embed multiple inputs in a single request, pass an array of strings or array
+ of token arrays. The input must not exceed the max input tokens for the model,
+ cannot be an empty string, and any array must be 2048 dimensions or less.
+ """
+
+ model: Required[str]
+ """ID of the model to use."""
+
+ dimensions: int
+ """The number of dimensions to return the embeddings in."""
+
+ encoding_format: Literal["float", "base64"]
+ """The format to return the embeddings in."""
+
+ user: Optional[str]
+ """
+ A unique identifier representing your end-user, which can help us monitor and
+ detect abuse.
+ """

src/groq/types/embedding_create_response.py

@@ -0,0 +1,45 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import List, Union
+from typing_extensions import Literal
+
+from .._models import BaseModel
+
+__all__ = ["EmbeddingCreateResponse", "Data", "Usage"]
+
+
+class Data(BaseModel):
+ embedding: Union[List[float], str]
+ """The embedding vector, which is a list of floats.
+
+ The length of vector depends on the model as listed in the
+ [embedding guide](/docs/guides/embeddings).
+ """
+
+ index: int
+ """The index of the embedding in the list of embeddings."""
+
+ object: Literal["embedding"]
+ """The object type, which is always "embedding"."""
+
+
+class Usage(BaseModel):
+ prompt_tokens: int
+ """The number of tokens used by the prompt."""
+
+ total_tokens: int
+ """The total number of tokens used by the request."""
+
+
+class EmbeddingCreateResponse(BaseModel):
+ data: List[Data]
+ """The list of embeddings generated by the model."""
+
+ model: str
+ """The name of the model used to generate the embedding."""
+
+ object: Literal["list"]
+ """The object type, which is always "list"."""
+
+ usage: Usage
+ """The usage information for the request."""